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"In this self-consistent monograph, the author gathers and describes different mathematical techniques and combines all together to form practical procedures for the inverse analyses. It puts together topics coming from mathematical programming, with soft computing and Proper Orthogonal Decomposition, in order to show, in the context of structural analyses, how the things work and what are the main problems one needs to tackle. Throughout the book a number of examples and exercises."

"This book provides an extensive introduction to the mechanics of anti-sandwiches: non-classical composites with multiple homogeneous layers but widely differing parameters concerning their geometry and materials. Therefore, they require special attention in the context of structural mechanics. The theoretical framework presented here is based on a five parametric, planar continuum, which is a pragmatic version of the COSSERAT shell. The direct approach used here is enlarged where constraints are introduced to couple layers and furnish a layer-wise theory. Restrictions are made in terms of linearity-geometrical and physical. After having defined appropriate variables for the kinematics and kinetics, linear elastic material behaviour is considered, where the constitutive tensors are introduced in the context of isotropy. The basics are presented in a clear and distinct manner using index-free tensor notation. This format is simple, concise, and practical. Closed-form solutions of such boundary value problems are usually associated with serious limitations on the boundary conditions, which constitutes a serious disadvantage. To construct approximate solutions, a variational method is employed as the basis for computational procedures where the Finite Element Method is applied. Therefore, the introduction of the vector-matrix notation is convenient. Based on the plane considerations, a finite eight-node SERENDIPITY element with enlarged degrees of freedom is realised. To avoid artificial stiffening effects, various integration types are applied, and the solutions generated are subsequently verified with closed-form solutions for monolithic limiting cases.Within this setting, it is possible to efficiently calculate the global structural behaviour of Anti-Sandwiches, at least up to a certain degree. The power of the proposed method in combination with the numerical solution approach is demonstrated for several case and parameter studies. In this regard, the optimal geometrical and material parameters to increase stiffness are analysed and the results for the kinematic and kinetic quantities are discussed. "

"While the prediction of observations is a forward problem, the use of actual observations to infer the properties of a model is an inverse problem. Inverse problems are difficult because they may not have a unique solution. The description of uncertainties plays a central role in the theory, which is based on probability theory. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, and many of the arguments are heuristic."

"Analisis regresi digunakan untuk mengetahui hubungan antara satu variabel respon dan satu atau lebih variabel penjelas. Ketika variabel respon berupa data count yaitu data yang berupa bilangan bulat non-negatif, analisis regresi yang sering digunakan adalah analisis regresi Poisson. Pada regresi Poisson terdapat asumsi kesamaan nilai mean dengan nilai variansinya. Dalam data count sering didapati kondisi dimana nilai variansi lebih besar dari nilai meannya atau disebut overdispersi. Pada data yang overdispersi, regresi Poisson kurang tepat jika digunakan karena nilai standard error dari taksiran parameter yang dihasilkan akanunderestimate sehingga beresiko memberikan kesimpulan yang tidak tepat. Model regresi Poisson-Inverse Gaussian dapat digunakan pada data count yang overdispersi dan memiliki tail panjang. Penaksiran parameter model regresi Poisson-Inverse Gaussian menggunakan metode maksimum likelihood dan solusi dari fungsi log -likelihood-nya menggunakan pendekatan numerik yaitu Newton-Raphson. Uji kesesuaian model yang digunakan mencakup statistik pseudo R-Squared, uji rasio likelihood, dan Uji Wald.

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Regression analysis is used to investigate the relationship between one response variable and one or more regressor variables. If the response variable is count data, that has non negative integer value, the regression analysis that usually used is Poisson Regression. Poisson regression has an assumption that mean of response variable equal to its variance. On count data frequently found that the variance is greater than mean, or called overdispersion. On overdispersion case, poisson regression is inconvenient to used because it may underestimate the standard error of regression parameters and consequently it risk to give misleading inference. Poisson Inverse Gaussian regression model can be used on overdispersion and long tail count data. Parameter estimation of Poisson Inverse Gaussian Regression Model can be obtained through the maximum likelihood method and the solution of log likelihood function may be solved by using numerical method called Newton Raphson. Goodness of fit testing of this model includes pseudo R Squared, rasio likelihood test, and Wald test."